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Privacy in embedding-based neural networks by means of homomorphic encryption

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Start date : 01/01/2020

offer n° PsD-DRT-20-0021

AI presently emerges as the killer application of homomorphic encryption or FHE. Indeed, this kind of cryptography, which allows to perform general calculations directly over encrypted data, has the potential of bringing privacy-by-construction for either or both user or model data, depending on the application scenario. In the longer term, FHE may also help protect training data, unleashing new usages in training data sharing and collaborative AI model building. In this context, the present postdoctoral offer aims at investigating the practical relevance of homomorphic encryption in the case of a specific kind of neural networks, the so-called embedding-based networks, which, for intrinsic reasons, both are favorable to good homomorphic execution performances and enjoy a wide spectrum of applications. Thus, this postdoctorate will study the theoretical and practical aspects cropping up in several FHE integration scenarios and will also lead to prototyping work on a best-in-class open-source speech recognition system using an embedding-based network.

  • Keywords : Engineering sciences, Computer science and software, DACLE, Leti
  • Laboratory : DACLE / Leti
  • CEA code : PsD-DRT-20-0021
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